Pattern Recognition (for CS)

Faculteit Science and Engineering
Jaar 2022/23
Vakcode WMCS011-05
Vaknaam Pattern Recognition (for CS)
Niveau(s) master
Voertaal Engels
Periode semester I b

Uitgebreide vaknaam Pattern Recognition (for CS)
Leerdoelen At the end of the course, the student is able to:
1) Master various concepts and techniques for pattern recognition
2) get familiar with various applications
Omschrijving This course provides an introduction to the theory and practice of pattern recognition. It is the research area that studies the design and operation of systems that detect, identify, recognise or classify patterns in data.

Important application domains are image analysis (e.g. licence plate recognition or various medical applications), computer vision, speech analysis, man and machine diagnostics, person identification (e.g. by iris or fingerprint), spam filtering, industrial inspection, financial data analysis and forecast, genetics. Generally, pattern recognition includes techniques such as feature extraction, classification, and error estimation. The course presents various classification techniques, e.g. Statistical decision theory (hypothesis testing), pattern recognition by transforms, SIFT descriptors for keypoint detection, Viola/Jones face detector, Independent Component Analysis, ANN, CNN, GAN, etc.. Various practical applications are presented throughout the course.
Uren per week
Onderwijsvorm Hoorcollege (LC), Practisch werk (PRC)
Toetsvorm Practisch werk (PR), Schriftelijk tentamen (WE)
(Your final mark will be computed as a weighted average of your marks for the practicals (40%) and the written examination (60%). In order to pass, you must get a sufficient (i.e. at least 5.5) for each of these separately. You will be admitted to the written (re-)examination only if you fulfill the requirements for the practicals.)
Vaksoort master
Coördinator J. Guo, PhD.
Docent(en) J. Guo, PhD.
Verplichte literatuur
Titel Auteur ISBN Prijs
see list of recommended literature in Nestor
Entreevoorwaarden Students who did their bachelor in Groningen and followed the course 'Introduction to Machine learning' are familiar with
programming in Matlab/Python. Students who come from outside the RUG or other disciplines should prepare themselves for
programming in Matlab/Python for the computer practicals.
Opmerkingen Some CS master courses — including Pattern Recognition — have limited enrollment:
- CS students can always enter the course, regardless of whether the course is mandatory for them or not.
- The number of enrolments for other non-CS students is limited. These students need to meet the course prerequisite requirements as mentioned on Ocasys. Priority is given to students for which the course is an official elective (see list below).
- An exception can be made for exchange students, if they have a CS background: please contact the FSE International Office. See here for more info about the enrollment procedure.
Opgenomen in
Opleiding Jaar Periode Type
MSc Applied mathematics  (Specialisatie: Statistics and Data Science) - semester I b keuzegroep
MSc Astronomy: Quantum Universe  (Optional Courses in Data Science (DS)) - semester I b keuze
MSc Computing Science: Data Science and Systems Complexity  (Compulsory course units) 1 semester I b verplicht
MSc Computing Science: Intelligent Systems and Visual Computing  (Compulsory course units) 1 semester I b verplicht
MSc Computing Science: Science Business and Policy  (Elective course units) 1 semester I b keuze
MSc Computing Science: Software Engineering and Distributed Systems  (Guided choice course units) - semester I b keuze
MSc Courses for Exchange Students: AI - Computing Science - Mathematics - semester I b
MSc Mathematics: Science, Business and Policy  (Science, Business and Policy: Statistics and Big Data) - semester I b keuzegroep
MSc Mathematics: Statistics and Big Data  (MSc Mathematics: Statistics and Big Data) - semester I b keuze